Overview

Dataset statistics

Number of variables37
Number of observations2922
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory488.0 KiB
Average record size in memory171.0 B

Variable types

DateTime1
Numeric15
Categorical21

Alerts

vehicle_operation is highly overall correlated with reception_cases and 6 other fieldsHigh correlation
reception_cases is highly overall correlated with vehicle_operation and 6 other fieldsHigh correlation
boarding_cases is highly overall correlated with vehicle_operation and 6 other fieldsHigh correlation
average_wait_time is highly overall correlated with target and 2 other fieldsHigh correlation
average_fare is highly overall correlated with vehicle_operation and 6 other fieldsHigh correlation
average_boarding_distance is highly overall correlated with vehicle_operation and 6 other fieldsHigh correlation
target is highly overall correlated with average_wait_time and 2 other fieldsHigh correlation
temp_max_forecast is highly overall correlated with temp_min_forecast and 1 other fieldsHigh correlation
temp_min_forecast is highly overall correlated with temp_max_forecast and 2 other fieldsHigh correlation
rain(mm)_forecast is highly overall correlated with humidity_max(%)_forecast and 1 other fieldsHigh correlation
humidity_max(%)_forecast is highly overall correlated with rain(mm)_forecast and 1 other fieldsHigh correlation
humidity_min(%)_forecast is highly overall correlated with rain(mm)_forecast and 1 other fieldsHigh correlation
Boarding rate is highly overall correlated with average_wait_time and 2 other fieldsHigh correlation
average_wait_time_7 is highly overall correlated with average_wait_time and 2 other fieldsHigh correlation
holiday is highly overall correlated with vehicle_operation and 6 other fieldsHigh correlation
holiday eve is highly overall correlated with day_Friday and 1 other fieldsHigh correlation
month_1 is highly overall correlated with temp_max_forecastHigh correlation
month_7 is highly overall correlated with temp_min_forecastHigh correlation
month_8 is highly overall correlated with temp_min_forecastHigh correlation
day_Friday is highly overall correlated with holiday eveHigh correlation
day_Saturday is highly overall correlated with vehicle_operation and 6 other fieldsHigh correlation
day_Sunday is highly overall correlated with vehicle_operation and 5 other fieldsHigh correlation
month_1 is highly imbalanced (58.1%)Imbalance
month_2 is highly imbalanced (60.7%)Imbalance
month_3 is highly imbalanced (58.1%)Imbalance
month_4 is highly imbalanced (59.0%)Imbalance
month_5 is highly imbalanced (58.1%)Imbalance
month_6 is highly imbalanced (59.0%)Imbalance
month_7 is highly imbalanced (58.1%)Imbalance
month_8 is highly imbalanced (58.1%)Imbalance
month_9 is highly imbalanced (59.0%)Imbalance
month_10 is highly imbalanced (58.0%)Imbalance
month_11 is highly imbalanced (59.0%)Imbalance
month_12 is highly imbalanced (58.2%)Imbalance
rain(mm)_forecast has 2088 (71.5%) zerosZeros

Reproduction

Analysis started2023-04-19 08:03:55.263455
Analysis finished2023-04-19 08:04:59.107359
Duration1 minute and 3.84 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

Date
Date

Distinct2921
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size45.7 KiB
Minimum2015-01-01 00:00:00
Maximum2022-12-30 00:00:00
2023-04-19T08:04:59.237373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:59.506591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

vehicle_operation
Real number (ℝ)

Distinct477
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean418.70089
Minimum132
Maximum1413
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.7 KiB
2023-04-19T08:04:59.746567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum132
5-th percentile203
Q1278
median454
Q3522
95-th percentile594
Maximum1413
Range1281
Interquartile range (IQR)244

Descriptive statistics

Standard deviation133.75637
Coefficient of variation (CV)0.31945566
Kurtosis-0.11326761
Mean418.70089
Median Absolute Deviation (MAD)87
Skewness-0.19426786
Sum1223444
Variance17890.766
MonotonicityNot monotonic
2023-04-19T08:05:00.023304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
442 21
 
0.7%
218 20
 
0.7%
458 19
 
0.7%
522 18
 
0.6%
524 17
 
0.6%
219 17
 
0.6%
227 16
 
0.5%
459 16
 
0.5%
456 16
 
0.5%
479 15
 
0.5%
Other values (467) 2747
94.0%
ValueCountFrequency (%)
132 1
 
< 0.1%
139 1
 
< 0.1%
157 1
 
< 0.1%
161 1
 
< 0.1%
163 1
 
< 0.1%
164 2
0.1%
167 2
0.1%
171 3
0.1%
173 1
 
< 0.1%
174 2
0.1%
ValueCountFrequency (%)
1413 1
< 0.1%
814 1
< 0.1%
700 1
< 0.1%
697 1
< 0.1%
692 1
< 0.1%
690 1
< 0.1%
689 1
< 0.1%
685 1
< 0.1%
684 1
< 0.1%
682 1
< 0.1%

reception_cases
Real number (ℝ)

Distinct1820
Distinct (%)62.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3925.4398
Minimum527
Maximum6182
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.7 KiB
2023-04-19T08:05:00.291977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum527
5-th percentile1441.3
Q12160.5
median4720.5
Q35110
95-th percentile5571.95
Maximum6182
Range5655
Interquartile range (IQR)2949.5

Descriptive statistics

Standard deviation1509.9648
Coefficient of variation (CV)0.38466132
Kurtosis-1.2958201
Mean3925.4398
Median Absolute Deviation (MAD)573.5
Skewness-0.58264008
Sum11470135
Variance2279993.8
MonotonicityNot monotonic
2023-04-19T08:05:00.538129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5014 8
 
0.3%
5050 8
 
0.3%
5003 8
 
0.3%
4948 7
 
0.2%
1841 7
 
0.2%
4881 7
 
0.2%
5178 7
 
0.2%
5101 6
 
0.2%
4939 6
 
0.2%
5011 6
 
0.2%
Other values (1810) 2852
97.6%
ValueCountFrequency (%)
527 1
< 0.1%
590 1
< 0.1%
591 1
< 0.1%
596 1
< 0.1%
609 1
< 0.1%
618 1
< 0.1%
619 1
< 0.1%
623 1
< 0.1%
624 1
< 0.1%
626 1
< 0.1%
ValueCountFrequency (%)
6182 1
< 0.1%
6164 1
< 0.1%
6134 1
< 0.1%
6133 1
< 0.1%
6128 1
< 0.1%
6100 1
< 0.1%
6093 1
< 0.1%
6092 1
< 0.1%
6075 1
< 0.1%
6072 1
< 0.1%

boarding_cases
Real number (ℝ)

Distinct1657
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3283.7399
Minimum462
Maximum5189
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.7 KiB
2023-04-19T08:05:00.787921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum462
5-th percentile1282.4
Q11778.25
median3932.5
Q34241
95-th percentile4668
Maximum5189
Range4727
Interquartile range (IQR)2462.75

Descriptive statistics

Standard deviation1249.378
Coefficient of variation (CV)0.3804741
Kurtosis-1.3284619
Mean3283.7399
Median Absolute Deviation (MAD)512.5
Skewness-0.57033601
Sum9595088
Variance1560945.3
MonotonicityNot monotonic
2023-04-19T08:05:01.048248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4180 7
 
0.2%
4090 7
 
0.2%
4150 7
 
0.2%
4083 7
 
0.2%
1604 7
 
0.2%
4248 6
 
0.2%
4049 6
 
0.2%
1626 6
 
0.2%
4373 6
 
0.2%
3897 6
 
0.2%
Other values (1647) 2857
97.8%
ValueCountFrequency (%)
462 1
< 0.1%
541 1
< 0.1%
550 1
< 0.1%
551 1
< 0.1%
570 1
< 0.1%
580 2
0.1%
582 1
< 0.1%
586 1
< 0.1%
591 1
< 0.1%
611 1
< 0.1%
ValueCountFrequency (%)
5189 1
< 0.1%
5176 1
< 0.1%
5151 1
< 0.1%
5130 1
< 0.1%
5129 1
< 0.1%
5120 1
< 0.1%
5106 1
< 0.1%
5098 1
< 0.1%
5087 1
< 0.1%
5083 1
< 0.1%

average_wait_time
Real number (ℝ)

Distinct582
Distinct (%)19.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.30243
Minimum17.2
Maximum96.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.7 KiB
2023-04-19T08:05:01.321100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum17.2
5-th percentile21.1
Q129.6
median38.2
Q348.6
95-th percentile66.5
Maximum96.1
Range78.9
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.101169
Coefficient of variation (CV)0.34988384
Kurtosis0.39366018
Mean40.30243
Median Absolute Deviation (MAD)9.3
Skewness0.76738927
Sum117763.7
Variance198.84296
MonotonicityNot monotonic
2023-04-19T08:05:01.602041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.9 18
 
0.6%
38.8 16
 
0.5%
35.2 15
 
0.5%
27.6 15
 
0.5%
34 14
 
0.5%
38.2 14
 
0.5%
40.4 14
 
0.5%
44.6 14
 
0.5%
36.8 14
 
0.5%
33.7 14
 
0.5%
Other values (572) 2774
94.9%
ValueCountFrequency (%)
17.2 1
 
< 0.1%
17.6 1
 
< 0.1%
17.7 1
 
< 0.1%
17.8 2
 
0.1%
18.2 4
0.1%
18.3 1
 
< 0.1%
18.4 4
0.1%
18.5 7
0.2%
18.6 2
 
0.1%
18.7 2
 
0.1%
ValueCountFrequency (%)
96.1 1
< 0.1%
96 1
< 0.1%
94.7 1
< 0.1%
93.4 1
< 0.1%
92.4 1
< 0.1%
91.8 1
< 0.1%
90.2 1
< 0.1%
89.8 1
< 0.1%
89.2 1
< 0.1%
89 2
0.1%

average_fare
Real number (ℝ)

Distinct411
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2304.3576
Minimum2131
Maximum2733
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.7 KiB
2023-04-19T08:05:01.873669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2131
5-th percentile2189.05
Q12228
median2257
Q32401
95-th percentile2504.95
Maximum2733
Range602
Interquartile range (IQR)173

Descriptive statistics

Standard deviation107.36985
Coefficient of variation (CV)0.046594263
Kurtosis-0.3027064
Mean2304.3576
Median Absolute Deviation (MAD)42
Skewness0.93703671
Sum6733333
Variance11528.284
MonotonicityNot monotonic
2023-04-19T08:05:02.147514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2255 36
 
1.2%
2240 35
 
1.2%
2244 34
 
1.2%
2252 33
 
1.1%
2251 29
 
1.0%
2241 29
 
1.0%
2236 29
 
1.0%
2239 29
 
1.0%
2233 27
 
0.9%
2242 27
 
0.9%
Other values (401) 2614
89.5%
ValueCountFrequency (%)
2131 1
< 0.1%
2141 1
< 0.1%
2144 1
< 0.1%
2146 1
< 0.1%
2149 1
< 0.1%
2150 1
< 0.1%
2151 1
< 0.1%
2152 2
0.1%
2156 1
< 0.1%
2157 1
< 0.1%
ValueCountFrequency (%)
2733 1
< 0.1%
2708 1
< 0.1%
2700 1
< 0.1%
2690 1
< 0.1%
2685 1
< 0.1%
2649 1
< 0.1%
2640 1
< 0.1%
2639 1
< 0.1%
2634 1
< 0.1%
2628 1
< 0.1%

average_boarding_distance
Real number (ℝ)

Distinct1726
Distinct (%)59.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9254.2912
Minimum7672
Maximum14136
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.7 KiB
2023-04-19T08:05:02.397134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum7672
5-th percentile8145.05
Q18521
median8821.5
Q310154
95-th percentile11166.95
Maximum14136
Range6464
Interquartile range (IQR)1633

Descriptive statistics

Standard deviation1020.236
Coefficient of variation (CV)0.11024464
Kurtosis-0.099169191
Mean9254.2912
Median Absolute Deviation (MAD)416
Skewness0.9538583
Sum27041039
Variance1040881.5
MonotonicityNot monotonic
2023-04-19T08:05:02.650358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8646 9
 
0.3%
8933 7
 
0.2%
8759 6
 
0.2%
8453 6
 
0.2%
8815 6
 
0.2%
8662 6
 
0.2%
8960 6
 
0.2%
8714 6
 
0.2%
8499 6
 
0.2%
8738 6
 
0.2%
Other values (1716) 2858
97.8%
ValueCountFrequency (%)
7672 1
< 0.1%
7695 1
< 0.1%
7748 1
< 0.1%
7768 1
< 0.1%
7797 1
< 0.1%
7806 1
< 0.1%
7808 1
< 0.1%
7824 1
< 0.1%
7832 1
< 0.1%
7847 1
< 0.1%
ValueCountFrequency (%)
14136 1
< 0.1%
13904 1
< 0.1%
13304 1
< 0.1%
13056 1
< 0.1%
12913 1
< 0.1%
12747 1
< 0.1%
12516 1
< 0.1%
12404 1
< 0.1%
12277 1
< 0.1%
12265 1
< 0.1%

target
Real number (ℝ)

Distinct582
Distinct (%)19.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.30616
Minimum17.2
Maximum96.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.7 KiB
2023-04-19T08:05:02.925701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum17.2
5-th percentile21.1
Q129.6
median38.2
Q348.6
95-th percentile66.5
Maximum96.1
Range78.9
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.097992
Coefficient of variation (CV)0.34977264
Kurtosis0.39512632
Mean40.30616
Median Absolute Deviation (MAD)9.3
Skewness0.7677273
Sum117774.6
Variance198.75338
MonotonicityNot monotonic
2023-04-19T08:05:03.180144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.9 18
 
0.6%
38.8 16
 
0.5%
33.7 15
 
0.5%
27.6 15
 
0.5%
35.2 15
 
0.5%
38.2 14
 
0.5%
40.4 14
 
0.5%
44.6 14
 
0.5%
36.8 14
 
0.5%
34 14
 
0.5%
Other values (572) 2773
94.9%
ValueCountFrequency (%)
17.2 1
 
< 0.1%
17.6 1
 
< 0.1%
17.7 1
 
< 0.1%
17.8 2
 
0.1%
18.2 4
0.1%
18.3 1
 
< 0.1%
18.4 4
0.1%
18.5 7
0.2%
18.6 2
 
0.1%
18.7 2
 
0.1%
ValueCountFrequency (%)
96.1 1
< 0.1%
96 1
< 0.1%
94.7 1
< 0.1%
93.4 1
< 0.1%
92.4 1
< 0.1%
91.8 1
< 0.1%
90.2 1
< 0.1%
89.8 1
< 0.1%
89.2 1
< 0.1%
89 2
0.1%

temp_max_forecast
Real number (ℝ)

Distinct431
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.0795
Minimum-11.2
Maximum39.4
Zeros2
Zeros (%)0.1%
Negative152
Negative (%)5.2%
Memory size45.7 KiB
2023-04-19T08:05:03.443223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-11.2
5-th percentile-0.2
Q19
median19.7
Q327.4
95-th percentile32.4
Maximum39.4
Range50.6
Interquartile range (IQR)18.4

Descriptive statistics

Standard deviation10.705421
Coefficient of variation (CV)0.59213036
Kurtosis-0.95591053
Mean18.0795
Median Absolute Deviation (MAD)8.6
Skewness-0.36018703
Sum52828.3
Variance114.60604
MonotonicityNot monotonic
2023-04-19T08:05:03.696494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.4 24
 
0.8%
26.4 23
 
0.8%
28.9 20
 
0.7%
27.5 20
 
0.7%
28.2 19
 
0.7%
27.2 19
 
0.7%
28.7 18
 
0.6%
27.9 18
 
0.6%
28 17
 
0.6%
6.7 17
 
0.6%
Other values (421) 2727
93.3%
ValueCountFrequency (%)
-11.2 1
< 0.1%
-11 1
< 0.1%
-10.8 2
0.1%
-9.6 2
0.1%
-9.2 1
< 0.1%
-8.3 2
0.1%
-7.7 1
< 0.1%
-7.6 1
< 0.1%
-7.4 1
< 0.1%
-7.3 1
< 0.1%
ValueCountFrequency (%)
39.4 1
 
< 0.1%
38 1
 
< 0.1%
37.9 1
 
< 0.1%
37.8 1
 
< 0.1%
37.4 3
0.1%
37.2 1
 
< 0.1%
36.8 1
 
< 0.1%
36.7 1
 
< 0.1%
36.5 2
0.1%
36.4 3
0.1%

temp_min_forecast
Real number (ℝ)

Distinct427
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.4150582
Minimum-18.5
Maximum30.4
Zeros6
Zeros (%)0.2%
Negative674
Negative (%)23.1%
Memory size45.7 KiB
2023-04-19T08:05:03.956101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-18.5
5-th percentile-8.3
Q10.5
median10
Q318.9
95-th percentile25.1
Maximum30.4
Range48.9
Interquartile range (IQR)18.4

Descriptive statistics

Standard deviation10.782829
Coefficient of variation (CV)1.1452748
Kurtosis-1.0417786
Mean9.4150582
Median Absolute Deviation (MAD)9.2
Skewness-0.19190222
Sum27510.8
Variance116.2694
MonotonicityNot monotonic
2023-04-19T08:05:04.207090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 18
 
0.6%
-0.3 17
 
0.6%
20.2 16
 
0.5%
19.5 16
 
0.5%
20.4 16
 
0.5%
20 16
 
0.5%
17.7 15
 
0.5%
18.2 15
 
0.5%
21.3 15
 
0.5%
7.7 14
 
0.5%
Other values (417) 2764
94.6%
ValueCountFrequency (%)
-18.5 1
 
< 0.1%
-18 1
 
< 0.1%
-17.8 1
 
< 0.1%
-16.4 3
0.1%
-16.2 1
 
< 0.1%
-15.9 1
 
< 0.1%
-15.5 1
 
< 0.1%
-15.4 1
 
< 0.1%
-15.1 1
 
< 0.1%
-15 1
 
< 0.1%
ValueCountFrequency (%)
30.4 1
 
< 0.1%
30.2 1
 
< 0.1%
29.2 1
 
< 0.1%
28.4 1
 
< 0.1%
28.3 1
 
< 0.1%
28.2 2
0.1%
28 2
0.1%
27.9 3
0.1%
27.8 2
0.1%
27.6 2
0.1%

rain(mm)_forecast
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct247
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3556126
Minimum0
Maximum178.9
Zeros2088
Zeros (%)71.5%
Negative0
Negative (%)0.0%
Memory size45.7 KiB
2023-04-19T08:05:04.467270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.3
95-th percentile18.99
Maximum178.9
Range178.9
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation12.595804
Coefficient of variation (CV)3.7536526
Kurtosis53.138199
Mean3.3556126
Median Absolute Deviation (MAD)0
Skewness6.4888036
Sum9805.1
Variance158.65427
MonotonicityNot monotonic
2023-04-19T08:05:04.712021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2088
71.5%
0.5 56
 
1.9%
0.1 48
 
1.6%
0.2 37
 
1.3%
1 30
 
1.0%
0.3 26
 
0.9%
1.5 24
 
0.8%
0.4 20
 
0.7%
2 18
 
0.6%
0.6 16
 
0.5%
Other values (237) 559
 
19.1%
ValueCountFrequency (%)
0 2088
71.5%
0.1 48
 
1.6%
0.2 37
 
1.3%
0.3 26
 
0.9%
0.4 20
 
0.7%
0.5 56
 
1.9%
0.6 16
 
0.5%
0.7 12
 
0.4%
0.8 4
 
0.1%
0.9 9
 
0.3%
ValueCountFrequency (%)
178.9 1
< 0.1%
152.5 1
< 0.1%
133.5 1
< 0.1%
128.8 1
< 0.1%
123.5 1
< 0.1%
114.5 1
< 0.1%
113.8 1
< 0.1%
111 1
< 0.1%
108.5 1
< 0.1%
108 1
< 0.1%

humidity_max(%)_forecast
Real number (ℝ)

Distinct70
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.099247
Minimum29
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.7 KiB
2023-04-19T08:05:04.976582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum29
5-th percentile53
Q170
median82
Q390
95-th percentile98
Maximum100
Range71
Interquartile range (IQR)20

Descriptive statistics

Standard deviation14.024203
Coefficient of variation (CV)0.17729882
Kurtosis-0.20390111
Mean79.099247
Median Absolute Deviation (MAD)10
Skewness-0.66879142
Sum231128
Variance196.67827
MonotonicityNot monotonic
2023-04-19T08:05:05.223004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86 100
 
3.4%
91 93
 
3.2%
89 90
 
3.1%
83 89
 
3.0%
81 88
 
3.0%
94 85
 
2.9%
95 82
 
2.8%
93 82
 
2.8%
92 82
 
2.8%
88 79
 
2.7%
Other values (60) 2052
70.2%
ValueCountFrequency (%)
29 1
 
< 0.1%
30 1
 
< 0.1%
33 2
 
0.1%
34 1
 
< 0.1%
35 1
 
< 0.1%
36 1
 
< 0.1%
37 4
0.1%
38 2
 
0.1%
39 4
0.1%
40 5
0.2%
ValueCountFrequency (%)
100 45
1.5%
99 41
1.4%
98 62
2.1%
97 77
2.6%
96 77
2.6%
95 82
2.8%
94 85
2.9%
93 82
2.8%
92 82
2.8%
91 93
3.2%

humidity_min(%)_forecast
Real number (ℝ)

Distinct89
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.116632
Minimum7
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.7 KiB
2023-04-19T08:05:05.575076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile18
Q129
median39
Q351
95-th percentile72.95
Maximum98
Range91
Interquartile range (IQR)22

Descriptive statistics

Standard deviation16.399444
Coefficient of variation (CV)0.39885183
Kurtosis0.07542595
Mean41.116632
Median Absolute Deviation (MAD)11
Skewness0.64271977
Sum120142.8
Variance268.94176
MonotonicityNot monotonic
2023-04-19T08:05:06.049661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 90
 
3.1%
37 83
 
2.8%
29 82
 
2.8%
39 79
 
2.7%
33 74
 
2.5%
34 74
 
2.5%
30 72
 
2.5%
27 71
 
2.4%
25 70
 
2.4%
43 70
 
2.4%
Other values (79) 2157
73.8%
ValueCountFrequency (%)
7 1
 
< 0.1%
9 1
 
< 0.1%
10 3
 
0.1%
11 6
 
0.2%
12 4
 
0.1%
13 8
 
0.3%
14 20
0.7%
15 13
0.4%
16 28
1.0%
17 31
1.1%
ValueCountFrequency (%)
98 1
 
< 0.1%
95 3
0.1%
94 1
 
< 0.1%
93 1
 
< 0.1%
92 6
0.2%
91 2
 
0.1%
90 4
0.1%
89 6
0.2%
87 5
0.2%
86 3
0.1%

sunshine(MJ/m2)_forecast
Real number (ℝ)

Distinct1699
Distinct (%)58.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.496472
Minimum0
Maximum30.79
Zeros4
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size45.7 KiB
2023-04-19T08:05:06.540004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q18.2425
median12.67
Q318.58
95-th percentile25.7395
Maximum30.79
Range30.79
Interquartile range (IQR)10.3375

Descriptive statistics

Standard deviation6.9401649
Coefficient of variation (CV)0.51422069
Kurtosis-0.79086337
Mean13.496472
Median Absolute Deviation (MAD)5.2
Skewness0.27946103
Sum39436.69
Variance48.165889
MonotonicityNot monotonic
2023-04-19T08:05:06.999888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.46 8
 
0.3%
9.97 7
 
0.2%
14.1 6
 
0.2%
8.93 6
 
0.2%
10.76 6
 
0.2%
12.29 6
 
0.2%
12.08 6
 
0.2%
3.06 6
 
0.2%
20.45 6
 
0.2%
9.29 6
 
0.2%
Other values (1689) 2859
97.8%
ValueCountFrequency (%)
0 4
0.1%
0.54 1
 
< 0.1%
0.65 1
 
< 0.1%
0.7 1
 
< 0.1%
0.75 1
 
< 0.1%
0.84 1
 
< 0.1%
0.86 1
 
< 0.1%
0.96 1
 
< 0.1%
1.06 1
 
< 0.1%
1.07 1
 
< 0.1%
ValueCountFrequency (%)
30.79 1
< 0.1%
30.44 1
< 0.1%
30.35 1
< 0.1%
30.06 1
< 0.1%
30.01 1
< 0.1%
29.93 1
< 0.1%
29.7 1
< 0.1%
29.45 1
< 0.1%
29.35 1
< 0.1%
29.3 1
< 0.1%

Boarding rate
Real number (ℝ)

Distinct2909
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.84156761
Minimum0.59699625
Maximum0.96884422
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.7 KiB
2023-04-19T08:05:07.396673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.59699625
5-th percentile0.75220925
Q10.80596003
median0.84561544
Q30.87772009
95-th percentile0.92063806
Maximum0.96884422
Range0.37184798
Interquartile range (IQR)0.071760056

Descriptive statistics

Standard deviation0.052175765
Coefficient of variation (CV)0.061998305
Kurtosis0.52378881
Mean0.84156761
Median Absolute Deviation (MAD)0.035570865
Skewness-0.44536384
Sum2459.0606
Variance0.0027223105
MonotonicityNot monotonic
2023-04-19T08:05:07.815007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8460732984 2
 
0.1%
0.8954593453 2
 
0.1%
0.8333333333 2
 
0.1%
0.9169491525 2
 
0.1%
0.9381132075 2
 
0.1%
0.8719211823 2
 
0.1%
0.8613861386 2
 
0.1%
0.9111461619 2
 
0.1%
0.8518376723 2
 
0.1%
0.8866354654 2
 
0.1%
Other values (2899) 2902
99.3%
ValueCountFrequency (%)
0.5969962453 1
< 0.1%
0.6033402923 1
< 0.1%
0.60789801 1
< 0.1%
0.6121105748 1
< 0.1%
0.6141219385 1
< 0.1%
0.6313932981 1
< 0.1%
0.6483679525 1
< 0.1%
0.6541929666 1
< 0.1%
0.6624696267 1
< 0.1%
0.6728810302 1
< 0.1%
ValueCountFrequency (%)
0.9688442211 1
< 0.1%
0.9656160458 1
< 0.1%
0.9655737705 1
< 0.1%
0.9634655532 1
< 0.1%
0.9611280488 1
< 0.1%
0.9603633361 1
< 0.1%
0.9567765568 1
< 0.1%
0.9561965812 1
< 0.1%
0.9560085837 1
< 0.1%
0.9560029828 1
< 0.1%

holiday
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.7 KiB
0
2005 
1
917 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2922
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 2005
68.6%
1 917
31.4%

Length

2023-04-19T08:05:08.161432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T08:05:08.504781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2005
68.6%
1 917
31.4%

Most occurring characters

ValueCountFrequency (%)
0 2005
68.6%
1 917
31.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2922
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2005
68.6%
1 917
31.4%

Most occurring scripts

ValueCountFrequency (%)
Common 2922
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2005
68.6%
1 917
31.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2922
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2005
68.6%
1 917
31.4%

average_wait_time_7
Real number (ℝ)

Distinct2100
Distinct (%)71.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.316706
Minimum18.8
Maximum83.928571
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.7 KiB
2023-04-19T08:05:08.902944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum18.8
5-th percentile22.687143
Q131.328571
median39.085714
Q346.964286
95-th percentile61.042857
Maximum83.928571
Range65.128571
Interquartile range (IQR)15.635714

Descriptive statistics

Standard deviation12.019928
Coefficient of variation (CV)0.29813765
Kurtosis0.093827838
Mean40.316706
Median Absolute Deviation (MAD)7.8142857
Skewness0.57695062
Sum117805.41
Variance144.47866
MonotonicityNot monotonic
2023-04-19T08:05:09.387686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.31670586 6
 
0.2%
41.02857143 6
 
0.2%
38.28571429 5
 
0.2%
38.01428571 5
 
0.2%
31.57142857 5
 
0.2%
39.81428571 5
 
0.2%
36.3 5
 
0.2%
38.5 5
 
0.2%
42.17142857 4
 
0.1%
56.3 4
 
0.1%
Other values (2090) 2872
98.3%
ValueCountFrequency (%)
18.8 1
 
< 0.1%
18.98571429 1
 
< 0.1%
19.12857143 1
 
< 0.1%
19.17142857 1
 
< 0.1%
19.24285714 1
 
< 0.1%
19.25714286 1
 
< 0.1%
19.27142857 1
 
< 0.1%
19.28571429 2
0.1%
19.3 3
0.1%
19.38571429 1
 
< 0.1%
ValueCountFrequency (%)
83.92857143 1
< 0.1%
83.32857143 1
< 0.1%
83.3 1
< 0.1%
82.68571429 1
< 0.1%
82.47142857 1
< 0.1%
82.45714286 1
< 0.1%
81.58571429 1
< 0.1%
81.14285714 1
< 0.1%
80.9 1
< 0.1%
80.72857143 1
< 0.1%

holiday eve
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.7 KiB
0.0
2005 
1.0
917 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters8766
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2005
68.6%
1.0 917
31.4%

Length

2023-04-19T08:05:09.868285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T08:05:10.075819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2005
68.6%
1.0 917
31.4%

Most occurring characters

ValueCountFrequency (%)
0 4927
56.2%
. 2922
33.3%
1 917
 
10.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5844
66.7%
Other Punctuation 2922
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4927
84.3%
1 917
 
15.7%
Other Punctuation
ValueCountFrequency (%)
. 2922
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8766
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4927
56.2%
. 2922
33.3%
1 917
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4927
56.2%
. 2922
33.3%
1 917
 
10.5%

month_1
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.7 KiB
0
2674 
1
 
248

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2922
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 2674
91.5%
1 248
 
8.5%

Length

2023-04-19T08:05:10.254493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T08:05:10.448586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2674
91.5%
1 248
 
8.5%

Most occurring characters

ValueCountFrequency (%)
0 2674
91.5%
1 248
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2922
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2674
91.5%
1 248
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
Common 2922
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2674
91.5%
1 248
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2922
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2674
91.5%
1 248
 
8.5%

month_2
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.7 KiB
0
2696 
1
 
226

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2922
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2696
92.3%
1 226
 
7.7%

Length

2023-04-19T08:05:10.615396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T08:05:10.806563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2696
92.3%
1 226
 
7.7%

Most occurring characters

ValueCountFrequency (%)
0 2696
92.3%
1 226
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2922
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2696
92.3%
1 226
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
Common 2922
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2696
92.3%
1 226
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2922
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2696
92.3%
1 226
 
7.7%

month_3
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.7 KiB
0
2674 
1
 
248

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2922
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2674
91.5%
1 248
 
8.5%

Length

2023-04-19T08:05:10.971339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T08:05:11.169117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2674
91.5%
1 248
 
8.5%

Most occurring characters

ValueCountFrequency (%)
0 2674
91.5%
1 248
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2922
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2674
91.5%
1 248
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
Common 2922
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2674
91.5%
1 248
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2922
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2674
91.5%
1 248
 
8.5%

month_4
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.7 KiB
0
2682 
1
 
240

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2922
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2682
91.8%
1 240
 
8.2%

Length

2023-04-19T08:05:11.330025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T08:05:11.530209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2682
91.8%
1 240
 
8.2%

Most occurring characters

ValueCountFrequency (%)
0 2682
91.8%
1 240
 
8.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2922
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2682
91.8%
1 240
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
Common 2922
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2682
91.8%
1 240
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2922
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2682
91.8%
1 240
 
8.2%

month_5
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.7 KiB
0
2674 
1
 
248

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2922
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2674
91.5%
1 248
 
8.5%

Length

2023-04-19T08:05:11.696080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T08:05:11.895311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2674
91.5%
1 248
 
8.5%

Most occurring characters

ValueCountFrequency (%)
0 2674
91.5%
1 248
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2922
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2674
91.5%
1 248
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
Common 2922
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2674
91.5%
1 248
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2922
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2674
91.5%
1 248
 
8.5%

month_6
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.7 KiB
0
2682 
1
 
240

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2922
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2682
91.8%
1 240
 
8.2%

Length

2023-04-19T08:05:12.055448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T08:05:12.253088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2682
91.8%
1 240
 
8.2%

Most occurring characters

ValueCountFrequency (%)
0 2682
91.8%
1 240
 
8.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2922
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2682
91.8%
1 240
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
Common 2922
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2682
91.8%
1 240
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2922
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2682
91.8%
1 240
 
8.2%

month_7
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.7 KiB
0
2674 
1
 
248

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2922
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2674
91.5%
1 248
 
8.5%

Length

2023-04-19T08:05:12.417446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T08:05:12.609879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2674
91.5%
1 248
 
8.5%

Most occurring characters

ValueCountFrequency (%)
0 2674
91.5%
1 248
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2922
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2674
91.5%
1 248
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
Common 2922
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2674
91.5%
1 248
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2922
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2674
91.5%
1 248
 
8.5%

month_8
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.7 KiB
0
2674 
1
 
248

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2922
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2674
91.5%
1 248
 
8.5%

Length

2023-04-19T08:05:12.773149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T08:05:12.957512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2674
91.5%
1 248
 
8.5%

Most occurring characters

ValueCountFrequency (%)
0 2674
91.5%
1 248
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2922
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2674
91.5%
1 248
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
Common 2922
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2674
91.5%
1 248
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2922
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2674
91.5%
1 248
 
8.5%

month_9
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.7 KiB
0
2682 
1
 
240

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2922
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2682
91.8%
1 240
 
8.2%

Length

2023-04-19T08:05:13.117756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T08:05:13.310899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2682
91.8%
1 240
 
8.2%

Most occurring characters

ValueCountFrequency (%)
0 2682
91.8%
1 240
 
8.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2922
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2682
91.8%
1 240
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
Common 2922
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2682
91.8%
1 240
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2922
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2682
91.8%
1 240
 
8.2%

month_10
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.7 KiB
0
2673 
1
 
249

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2922
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2673
91.5%
1 249
 
8.5%

Length

2023-04-19T08:05:13.478440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T08:05:13.663132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2673
91.5%
1 249
 
8.5%

Most occurring characters

ValueCountFrequency (%)
0 2673
91.5%
1 249
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2922
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2673
91.5%
1 249
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
Common 2922
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2673
91.5%
1 249
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2922
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2673
91.5%
1 249
 
8.5%

month_11
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.7 KiB
0
2682 
1
 
240

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2922
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2682
91.8%
1 240
 
8.2%

Length

2023-04-19T08:05:13.824777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T08:05:14.018069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2682
91.8%
1 240
 
8.2%

Most occurring characters

ValueCountFrequency (%)
0 2682
91.8%
1 240
 
8.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2922
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2682
91.8%
1 240
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
Common 2922
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2682
91.8%
1 240
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2922
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2682
91.8%
1 240
 
8.2%

month_12
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.7 KiB
0
2675 
1
 
247

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2922
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2675
91.5%
1 247
 
8.5%

Length

2023-04-19T08:05:14.178916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T08:05:14.379742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2675
91.5%
1 247
 
8.5%

Most occurring characters

ValueCountFrequency (%)
0 2675
91.5%
1 247
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2922
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2675
91.5%
1 247
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
Common 2922
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2675
91.5%
1 247
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2922
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2675
91.5%
1 247
 
8.5%

day_Friday
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.7 KiB
0
2504 
1
418 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2922
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2504
85.7%
1 418
 
14.3%

Length

2023-04-19T08:05:14.563932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T08:05:14.773304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2504
85.7%
1 418
 
14.3%

Most occurring characters

ValueCountFrequency (%)
0 2504
85.7%
1 418
 
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2922
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2504
85.7%
1 418
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2922
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2504
85.7%
1 418
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2922
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2504
85.7%
1 418
 
14.3%

day_Monday
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.7 KiB
0
2505 
1
417 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2922
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 2505
85.7%
1 417
 
14.3%

Length

2023-04-19T08:05:14.932200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T08:05:15.120555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2505
85.7%
1 417
 
14.3%

Most occurring characters

ValueCountFrequency (%)
0 2505
85.7%
1 417
 
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2922
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2505
85.7%
1 417
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2922
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2505
85.7%
1 417
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2922
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2505
85.7%
1 417
 
14.3%

day_Saturday
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.7 KiB
0
2505 
1
417 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2922
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2505
85.7%
1 417
 
14.3%

Length

2023-04-19T08:05:15.299183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T08:05:15.496866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2505
85.7%
1 417
 
14.3%

Most occurring characters

ValueCountFrequency (%)
0 2505
85.7%
1 417
 
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2922
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2505
85.7%
1 417
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2922
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2505
85.7%
1 417
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2922
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2505
85.7%
1 417
 
14.3%

day_Sunday
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.7 KiB
0
2505 
1
417 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2922
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 2505
85.7%
1 417
 
14.3%

Length

2023-04-19T08:05:15.657581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T08:05:15.871352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2505
85.7%
1 417
 
14.3%

Most occurring characters

ValueCountFrequency (%)
0 2505
85.7%
1 417
 
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2922
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2505
85.7%
1 417
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2922
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2505
85.7%
1 417
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2922
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2505
85.7%
1 417
 
14.3%

day_Thursday
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.7 KiB
0
2504 
1
418 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2922
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2504
85.7%
1 418
 
14.3%

Length

2023-04-19T08:05:16.063002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T08:05:16.256799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2504
85.7%
1 418
 
14.3%

Most occurring characters

ValueCountFrequency (%)
0 2504
85.7%
1 418
 
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2922
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2504
85.7%
1 418
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2922
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2504
85.7%
1 418
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2922
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2504
85.7%
1 418
 
14.3%

day_Tuesday
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.7 KiB
0
2504 
1
418 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2922
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2504
85.7%
1 418
 
14.3%

Length

2023-04-19T08:05:16.441995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T08:05:16.639272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2504
85.7%
1 418
 
14.3%

Most occurring characters

ValueCountFrequency (%)
0 2504
85.7%
1 418
 
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2922
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2504
85.7%
1 418
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2922
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2504
85.7%
1 418
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2922
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2504
85.7%
1 418
 
14.3%

day_Wednesday
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.7 KiB
0
2505 
1
417 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2922
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2505
85.7%
1 417
 
14.3%

Length

2023-04-19T08:05:16.802802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T08:05:16.992018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2505
85.7%
1 417
 
14.3%

Most occurring characters

ValueCountFrequency (%)
0 2505
85.7%
1 417
 
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2922
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2505
85.7%
1 417
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2922
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2505
85.7%
1 417
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2922
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2505
85.7%
1 417
 
14.3%

Interactions

2023-04-19T08:04:52.947343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:00.329507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:03.859050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:07.884398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:12.031376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:15.279725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:18.775495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:22.895791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:27.285280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:30.470575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:33.650579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:38.476652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:42.442063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:45.561060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:48.759081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:53.257947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:00.532605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:04.073007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:08.232264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:12.239184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:15.500230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:18.979078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:23.224743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:27.506155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:30.686096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:33.863101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:38.817756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:42.639940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:45.769221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:48.997357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:53.568656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:00.738738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:04.277772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:08.580111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:12.452468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:15.697567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:19.181018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:23.613523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:27.714205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:30.880125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:34.516922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:39.141141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:42.842656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:45.968890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:49.215002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:53.920998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:00.932386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:04.469844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:08.932743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:12.646405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:15.891245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:19.377593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:23.955828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:27.911143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:31.088521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:34.715195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:39.484709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:43.025998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:46.175041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:49.417146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:54.314229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:01.175265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:04.698925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:09.317369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:12.863621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:16.117867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:19.626972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:24.310915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:28.136756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:31.309118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:34.945197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:39.877262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:43.240508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:46.409537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:49.656660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:54.690892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:01.382175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:04.894692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:09.631213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:13.070472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:16.678090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:19.838444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:24.660577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:28.331382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:31.509730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:35.149729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:40.196297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:43.453382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:46.621252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:49.879185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:55.077122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:01.598064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:05.115396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:10.004796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:13.294918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:16.875526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:20.059756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:25.056805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:28.549017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:31.739087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:35.368536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:40.481225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:43.655375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:46.831514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:50.090210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:55.370313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:02.139819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:05.352995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:10.292737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:13.535063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:17.102620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:20.286909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:25.409756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:28.771189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:31.958212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:35.635169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:40.705732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:43.874289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:47.072694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:50.317525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:55.571923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:02.350698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:05.555461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:10.573143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:13.748231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:17.298475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:20.509237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:25.674472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:28.975219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:32.205400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:35.920308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:40.906418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:44.081680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:47.277979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:50.623477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:55.782484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:02.560459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:05.809771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:10.765777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:13.951362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:17.505703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:20.846287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:25.894129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:29.171859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:32.395221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:36.271748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:41.114791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:44.279992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:47.491073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:50.989093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:56.006210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:02.773894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:06.177325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:10.981511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:14.172342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:17.716818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:21.188156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:26.109701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:29.383123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:32.612009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:36.634160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:41.364545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:44.507168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:47.703285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:51.364227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:56.232262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:02.994722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:06.555423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:11.216582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:14.392715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:17.933533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:21.531732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:26.338357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:29.594791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:32.823672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:37.009518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:41.582180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:44.719129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:47.910730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:51.692795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:56.441290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:03.205452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:06.897124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:11.432362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:14.611151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:18.128597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:21.876656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:26.553940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:29.800276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:33.035150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:37.364265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:41.786763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:44.899953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:48.108610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:52.033189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:56.664472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:03.421934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:07.162963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:11.631610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:14.825136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:18.326274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:22.229294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:26.778103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:30.025917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:33.229714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:37.723024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:42.001781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:45.107337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:48.318608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:52.356397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:56.905909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:03.637884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:07.539366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:11.838594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:15.057927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:18.546851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:22.604779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:27.046303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:30.255974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:33.455835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:38.107446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:42.226029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:45.327963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:48.550938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-19T08:04:52.638726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-04-19T08:05:17.201729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
vehicle_operationreception_casesboarding_casesaverage_wait_timeaverage_fareaverage_boarding_distancetargettemp_max_forecasttemp_min_forecastrain(mm)_forecasthumidity_max(%)_forecasthumidity_min(%)_forecastsunshine(MJ/m2)_forecastBoarding rateaverage_wait_time_7holidayholiday evemonth_1month_2month_3month_4month_5month_6month_7month_8month_9month_10month_11month_12day_Fridayday_Mondayday_Saturdayday_Sundayday_Thursdayday_Tuesdayday_Wednesday
vehicle_operation1.0000.7870.8280.056-0.740-0.7870.0720.1590.1660.0130.0710.1410.104-0.019-0.0710.9590.3130.0950.0920.0620.0940.0170.0630.0610.0400.1570.1260.0650.0820.2370.2160.6070.6570.2490.2420.217
reception_cases0.7871.0000.9240.456-0.649-0.6920.4360.1140.111-0.0030.0100.0210.043-0.3670.3310.9640.3640.1550.2070.1410.1380.0260.1230.0750.0340.1140.1170.0930.1090.2310.2210.6740.6830.2450.2370.224
boarding_cases0.8280.9241.0000.226-0.699-0.6980.2420.1290.119-0.0130.0130.0310.084-0.0710.0930.9660.3340.1540.1320.1180.1490.0530.1280.0710.0000.0650.1120.0830.1440.2510.2210.6330.6390.2420.2420.220
average_wait_time0.0560.4560.2261.0000.041-0.0320.7430.0730.067-0.000-0.023-0.070-0.034-0.7860.8330.2010.0920.1440.1420.1690.0000.0290.0000.0800.0560.0740.0990.1570.1990.0720.0290.1310.1720.0200.0490.018
average_fare-0.740-0.649-0.6990.0411.0000.9580.0090.1200.105-0.002-0.000-0.0650.051-0.0230.1690.9510.2890.1710.0910.0880.0850.1410.0520.0510.0920.1390.1570.0890.0860.2340.2540.5730.6590.2640.2440.225
average_boarding_distance-0.787-0.692-0.698-0.0320.9581.000-0.0530.1230.105-0.0010.002-0.0630.0350.0640.0950.9450.2940.1450.1060.0000.0990.0960.0370.0000.0330.1270.0920.0850.0890.2250.2500.5630.6540.2450.2510.226
target0.0720.4360.2420.7430.009-0.0531.0000.0760.0670.004-0.024-0.074-0.032-0.6170.7910.1650.2000.1360.1460.1630.0230.0570.0000.0740.0610.0770.1020.1670.1850.1300.0510.1720.0280.0720.0130.023
temp_max_forecast0.1590.1140.1290.0730.1200.1230.0761.0000.9560.0730.2580.3080.4840.0000.0740.0000.0000.5070.3790.3890.3420.3030.3590.4330.4510.3810.3760.3330.4380.0000.0000.0000.0000.0000.0000.000
temp_min_forecast0.1660.1110.1190.0670.1050.1050.0670.9561.0000.2260.3780.4840.300-0.0100.0660.0160.0050.4960.3980.4030.4010.4570.4320.5450.5510.4280.3590.3130.4170.0000.0000.0000.0000.0160.0000.000
rain(mm)_forecast0.013-0.003-0.013-0.000-0.002-0.0010.0040.0730.2261.0000.6630.505-0.432-0.020-0.0060.0380.0240.0290.0000.0000.0000.0000.0420.1270.1200.0000.0000.0000.0290.0000.0280.0340.0630.0000.0000.059
humidity_max(%)_forecast0.0710.0100.013-0.023-0.0000.002-0.0240.2580.3780.6631.0000.645-0.2500.028-0.0370.0000.0000.1630.1390.0990.0520.0600.0990.1600.1610.0620.0610.0000.1040.0000.0000.0170.0130.0000.0000.019
humidity_min(%)_forecast0.1410.0210.031-0.070-0.065-0.063-0.0740.3080.4840.5050.6451.000-0.4020.052-0.0810.0000.0000.1340.1500.2210.1930.0920.1210.2970.2540.1250.0840.0200.0590.0000.0000.0360.0210.0000.0000.000
sunshine(MJ/m2)_forecast0.1040.0430.084-0.0340.0510.035-0.0320.4840.300-0.432-0.250-0.4021.0000.095-0.0510.0000.0320.2900.2270.2310.2480.3910.2350.0740.1030.1790.1780.2510.3090.0390.0000.0000.0000.0000.0270.000
Boarding rate-0.019-0.367-0.071-0.786-0.0230.064-0.6170.000-0.010-0.0200.0280.0520.0951.000-0.7520.3290.2100.1440.1050.1750.0740.0370.0560.0460.0650.0550.0990.1090.1720.1010.0880.2010.4260.0850.0930.076
average_wait_time_7-0.0710.3310.0930.8330.1690.0950.7910.0740.066-0.006-0.037-0.081-0.051-0.7521.0000.0000.0000.1730.1750.2670.0960.0870.0470.0920.1130.0710.1320.2710.3160.0000.0000.0000.0000.0000.0000.000
holiday0.9590.9640.9660.2010.9510.9450.1650.0000.0160.0380.0000.0000.0000.3290.0001.0000.2900.0000.0100.0000.0000.0000.0000.0000.0000.0000.0160.0100.0000.2390.2380.6020.6020.2430.2390.238
holiday eve0.3130.3640.3340.0920.2890.2940.2000.0000.0050.0240.0000.0000.0320.2100.0000.2901.0000.0000.0290.0120.0000.0000.0000.0000.0000.0050.0060.0100.0000.6030.2400.6020.2380.2390.2370.245
month_10.0950.1550.1540.1440.1710.1450.1360.5070.4960.0290.1630.1340.2900.1440.1730.0000.0001.0000.0840.0890.0870.0890.0870.0890.0890.0870.0890.0870.0880.0000.0000.0000.0000.0000.0000.000
month_20.0920.2070.1320.1420.0910.1060.1460.3790.3980.0000.1390.1500.2270.1050.1750.0100.0290.0841.0000.0840.0820.0840.0820.0840.0840.0820.0840.0820.0840.0000.0000.0000.0000.0000.0000.000
month_30.0620.1410.1180.1690.0880.0000.1630.3890.4030.0000.0990.2210.2310.1750.2670.0000.0120.0890.0841.0000.0870.0890.0870.0890.0890.0870.0890.0870.0880.0000.0000.0000.0000.0000.0000.000
month_40.0940.1380.1490.0000.0850.0990.0230.3420.4010.0000.0520.1930.2480.0740.0960.0000.0000.0870.0820.0871.0000.0870.0850.0870.0870.0850.0870.0850.0870.0000.0000.0000.0000.0000.0000.000
month_50.0170.0260.0530.0290.1410.0960.0570.3030.4570.0000.0600.0920.3910.0370.0870.0000.0000.0890.0840.0890.0871.0000.0870.0890.0890.0870.0890.0870.0880.0000.0000.0000.0000.0000.0000.000
month_60.0630.1230.1280.0000.0520.0370.0000.3590.4320.0420.0990.1210.2350.0560.0470.0000.0000.0870.0820.0870.0850.0871.0000.0870.0870.0850.0870.0850.0870.0000.0000.0000.0000.0000.0000.000
month_70.0610.0750.0710.0800.0510.0000.0740.4330.5450.1270.1600.2970.0740.0460.0920.0000.0000.0890.0840.0890.0870.0890.0871.0000.0890.0870.0890.0870.0880.0000.0000.0000.0000.0000.0000.000
month_80.0400.0340.0000.0560.0920.0330.0610.4510.5510.1200.1610.2540.1030.0650.1130.0000.0000.0890.0840.0890.0870.0890.0870.0891.0000.0870.0890.0870.0880.0000.0000.0000.0000.0000.0000.000
month_90.1570.1140.0650.0740.1390.1270.0770.3810.4280.0000.0620.1250.1790.0550.0710.0000.0050.0870.0820.0870.0850.0870.0850.0870.0871.0000.0870.0850.0870.0000.0000.0000.0000.0000.0000.000
month_100.1260.1170.1120.0990.1570.0920.1020.3760.3590.0000.0610.0840.1780.0990.1320.0160.0060.0890.0840.0890.0870.0890.0870.0890.0890.0871.0000.0870.0890.0000.0000.0000.0000.0000.0000.000
month_110.0650.0930.0830.1570.0890.0850.1670.3330.3130.0000.0000.0200.2510.1090.2710.0100.0100.0870.0820.0870.0850.0870.0850.0870.0870.0850.0871.0000.0870.0000.0000.0000.0000.0000.0000.000
month_120.0820.1090.1440.1990.0860.0890.1850.4380.4170.0290.1040.0590.3090.1720.3160.0000.0000.0880.0840.0880.0870.0880.0870.0880.0880.0870.0890.0871.0000.0000.0000.0000.0000.0000.0000.000
day_Friday0.2370.2310.2510.0720.2340.2250.1300.0000.0000.0000.0000.0000.0390.1010.0000.2390.6030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.1640.1640.1640.1650.1650.164
day_Monday0.2160.2210.2210.0290.2540.2500.0510.0000.0000.0280.0000.0000.0000.0880.0000.2380.2400.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1641.0000.1640.1640.1640.1640.164
day_Saturday0.6070.6740.6330.1310.5730.5630.1720.0000.0000.0340.0170.0360.0000.2010.0000.6020.6020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1640.1641.0000.1640.1640.1640.164
day_Sunday0.6570.6830.6390.1720.6590.6540.0280.0000.0000.0630.0130.0210.0000.4260.0000.6020.2380.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1640.1640.1641.0000.1640.1640.164
day_Thursday0.2490.2450.2420.0200.2640.2450.0720.0000.0160.0000.0000.0000.0000.0850.0000.2430.2390.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1650.1640.1640.1641.0000.1650.164
day_Tuesday0.2420.2370.2420.0490.2440.2510.0130.0000.0000.0000.0000.0000.0270.0930.0000.2390.2370.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1650.1640.1640.1640.1651.0000.164
day_Wednesday0.2170.2240.2200.0180.2250.2260.0230.0000.0000.0590.0190.0000.0000.0760.0000.2380.2450.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1640.1640.1640.1640.1640.1641.000

Missing values

2023-04-19T08:04:57.870042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-19T08:04:58.735368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Datevehicle_operationreception_casesboarding_casesaverage_wait_timeaverage_fareaverage_boarding_distancetargettemp_max_forecasttemp_min_forecastrain(mm)_forecasthumidity_max(%)_forecasthumidity_min(%)_forecastsunshine(MJ/m2)_forecastBoarding rateholidayaverage_wait_time_7holiday evemonth_1month_2month_3month_4month_5month_6month_7month_8month_9month_10month_11month_12day_Fridayday_Mondayday_Saturdayday_Sundayday_Thursdayday_Tuesdayday_Wednesday
02015-01-01213102392423.224271076417.2-2.0-8.90.063.028.09.070.903226140.3167060.01000000000000000100
12015-01-024203158283917.22216861126.22.4-9.20.073.037.08.660.898987040.3167061.01000000000001000000
22015-01-032091648151426.223771019824.58.20.20.089.058.05.320.918689140.3167061.01000000000000010000
32015-01-041961646152624.524311095526.27.9-0.90.095.052.06.480.927096140.3167060.01000000000000001000
42015-01-054214250373026.22214866323.64.1-7.43.498.029.010.470.877647040.3167060.01000000000000100000
52015-01-064173991363323.62211854524.7-1.0-8.80.042.024.010.120.910298040.3167060.01000000000000000010
62015-01-074104085367624.72230864621.2-0.2-9.20.062.027.010.090.899878023.6571430.01000000000000000001
72015-01-084194030372821.22231868321.83.2-6.80.078.038.08.740.925062023.3714290.01000000000000000100
82015-01-094244167381321.82215850641.24.3-5.50.081.033.09.410.915047024.0285711.01000000000001000000
92015-01-102151916164541.224471112328.82.3-4.00.083.039.09.600.858559126.1714291.01000000000000010000
Datevehicle_operationreception_casesboarding_casesaverage_wait_timeaverage_fareaverage_boarding_distancetargettemp_max_forecasttemp_min_forecastrain(mm)_forecasthumidity_max(%)_forecasthumidity_min(%)_forecastsunshine(MJ/m2)_forecastBoarding rateholidayaverage_wait_time_7holiday evemonth_1month_2month_3month_4month_5month_6month_7month_8month_9month_10month_11month_12day_Fridayday_Mondayday_Saturdayday_Sundayday_Thursdayday_Tuesdayday_Wednesday
29122022-12-216285936481539.62150783256.4-2.6-10.90.075.045.010.050.811152042.9714290.00000000000010000001
29132022-12-226255899471656.42188805151.7-9.2-13.70.065.053.010.760.799458045.2428570.00000000000010000100
29142022-12-236075570436151.72159800329.4-2.1-13.50.069.040.010.920.782944046.2571431.00000000000011000000
29152022-12-243082279191029.42367980638.7-0.2-9.50.081.047.010.890.838087143.6142861.00000000000010010000
29162022-12-252171945158838.724161021239.21.5-7.90.085.046.08.270.816452145.1142860.00000000000010001000
29172022-12-266035555460539.22163788944.43.0-7.30.086.051.010.250.828983043.4857140.00000000000010100000
29182022-12-276695635465444.42198817844.8-0.3-5.40.192.040.010.860.825909042.7714290.00000000000010000010
29192022-12-286075654464844.82161788252.51.7-7.80.071.034.010.880.822073043.5142860.00000000000010000001
29202022-12-295815250424752.52229843338.32.1-4.00.087.038.010.840.808952042.9571430.00000000000010000100
29212022-12-306005293420038.32183815533.7-4.4-4.40.066.066.00.000.793501041.0428571.00000000000011000000